Steel Operations AI Domain Lead

HCL Technologies Limited

Wisconsin

Vor Ort

USD 180.000 - 240.000

Vollzeit

Vor 2 Tagen
Sei unter den ersten Bewerbenden

Eine vollständige Bewerbung in einer Minute

Maßgeschneiderter Lebenslauf und Anschreiben – versandbereit.

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Zusammenfassung

HCL Technologies Limited is seeking a hands-on leader to steward steel manufacturing AI initiatives on the Core team, ensuring models reflect metallurgical realities from melt shop to wire and casting lines.

You will bridge operations and data science, validate domain feasibility, and drive adoption across mills, focusing on measurable improvements in yield, energy use, throughput, and quality while upholding safety.

Qualifikationen

  • 15+ years in steel or metals manufacturing operations or metallurgy.
  • Deep command of steel process metallurgy and KPIs (yield, OEE, tap‑to‑tap, kWh/ton, electrode consumption).
  • Practical working knowledge of manufacturing systems and automation and how data is generated and used.
  • Proven record of driving measurable operational improvement on the plant floor.
  • Credibility with plant leadership and frontline teams alike.
  • Fluency with data, analytics, and AI to partner with data scientists (deep coding not required).

Aufgaben

  • Bring deep steel operations expertise to guide AI initiatives.
  • Identify and prioritize AI use cases by operational value.
  • Translate shop-floor problems into actionable statements for Pods.
  • Own domain logic behind business cases and ROI validation.

Kenntnisse

Steel operations expertise
Metallurgy knowledge
Manufacturing systems & automation
Operational improvement track record

Ausbildung

Engineering degree in metallurgical/materials/mechanical or related

Tools

MES
PI historians
SCADA / PLC
Level 1/2 automation

Jobbeschreibung

HCL Technologies Limited is seeking a hands-on leader to steward steel manufacturing AI initiatives on the Core team, ensuring models reflect metallurgical realities from melt shop to wire and casting lines.

You will bridge operations and data science, validate domain feasibility, and drive adoption across mills, focusing on measurable improvements in yield, energy use, throughput, and quality while upholding safety.

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